Let Alg denote the submitted executable, which performs the
segmentation of the noise free regions of the iris.

Let I={I1,…,In} be the data set containing the
input close-up iris images.

Let O={O1,…,On} be the output images correspondent to the above
described inputs, such that Alg(Ii)=Oi.

Let C={C1,…,Cn} bethe manually classified binary iris images, given by the NICE.I
Organizing Committee. It must be assumed that each Ci contains the perfect
iris segmentation and noise detection result for the input image Ii.

All the images of I, O and C have the same dimensions: c columns and r rows.

Two measures of evaluation will be
used:

The classification error rate(E1)
of the Alg participation on the
input image Ii (Ei)
is given by the proportion of correspondent disagreeing pixels (through the logical
exclusive-or operator) over all the image:

where O(c’,r’) and C(c’,r’)
are, respectively, pixels of the output and class images.

The classification error rate (E1) of the Alg participation is given by the
average of the errors on the input images Ei:

The value of (E1) is closed in the [0, 1] interval and will be the measure of evaluation and
classification of the NICE.I participations. In this context, “1” and “0”
will be respectively the worst and optimal values.

The second error measure aims to
compensate the disproportion between the apriori probabilities of “iris” and “non-iris
pixels in the images. The type-I and
type-II error rate (E2) of the image Eiis given by the average between the false-positives
(FPR) and false-negatives (FNR) rates:

Ei = 0.5 * FPR + 0.5 FNR

Similarly to the E1 error
rate, the final E2 error rate is given by the average of the
errors (Ei) on the input images.